Robustness of pattern scaled climate change scenarios for adaptation decision support

Abstract

Pattern scaling offers the promise of exploring spatial details of the climate system response to anthropogenic climate forcings without their full simulation by state-of-the-art Global Climate Models. The circumstances in which pattern scaling methods are capable of delivering on this promise are explored by quantifying its performance in an idealized setting. Given a large ensemble that is assumed to sample the full range of variability and provide quantitative decisionrelevant information, the soundness of applying the pattern scaling methodology to generate decision relevant climate scenarios is explored. Pattern scaling is not expected to reproduce its target exactly, of course, and its generic limitations have been well documented since it was first proposed. In this work, using as a particular example the quantification of the risk of heat waves in Southern Europe, it is shown that the magnitude of the error in the pattern scaled estimates can be significant enough to disqualify the use of this approach in quantitative decisionsupport. This suggests that future application of pattern scaling in climate science should provide decision makers not just a restatement of the assumptions made, but also evidence that the methodology is adequate for purpose in practice for the case under consideration.

Pattern scaling using ClimGen: monthly-resolution future climate scenarios including changes in the variability of precipitation

“…Ideally, both approaches would be utilised and compared where this is feasible. Lopez et al (2014) note some limitations of the PS approach. Their example highlights the need for careful consideration of variability as well as mean climate change, noting reasonable agreement for the frequency of hot summers in 30-year sequences but poor agreement when using 10-year sequences with only one realisation of climate variability.…”

Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C

“…Although also relying on the debatable assumption of scenario-independence of the projected signals, which does not fully hold in climate stabilization scenarios (Tebaldi and Arblaster, 2014), time-slicing avoids known short-comings of classical pattern scaling analysis. In particular, it allows one to capture non-linearities in extreme indices and precipitation-related signals that relate to nonlinear local feedbacks (Lopez et al, 2013) or large-scale circulation changes (Chadwick and Good, 2013;Hawkins et al, 2014).…”

Climate change impacts on meteorological, agricultural and hydrological droughts in China

“…The use of this period for near term prediction has also been adopted in many other studies (e.g. Hayhoe et al, 2004;Vicuna et al, 2007;Mariotti et al, 2008;Biasutti et al, 2012;Aich et al, 2014;Lopez et al, 2014).…”